A Gentle Introduction to Normality Tests in Python
Last Updated on August 8, 2019
An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods.
Parametric statistical methods assume that the data has a known and specific distribution, often a Gaussian distribution. If a data sample is not Gaussian, then the assumptions of parametric statistical tests are violated and nonparametric statistical methods must be used.
There are a range of techniques that you can use to check if your data sample deviates from a Gaussian distribution, called normality tests.
In this tutorial, you will discover the importance of checking whether a data sample deviates from the normal distribution and a suite of techniques that you can use to evaluate your data sample.
After completing this tutorial, you will know:
- How whether a sample is normal dictates the types of statistical methods to use with a data sample.
- Graphical methods for qualifying deviations from normal, such as histograms and the Q-Q plot.
- Statistical normality tests for quantifying deviations from normal.
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